The European Epilepsy Database.

We like to have our web presentation at the moment as simple as possible.

Please contact directly: matthias.duempelmann(at)uniklinik-freiburg.de

Nevertheless some info ...

The database developed within the EU funded the EPILEPSIAE project by three epilepsy centers in Coimbra, Paris and Freiburg.

The Database.

It's by orders of magnitude the largest
collection of long-term EEG recordings available

  • > 250 patient datasets
  • > 2500 Seizures
  • > 45.000 Hours of EEG
  • gold standard annotations
  • extensive clinical metadata

The European Epilepsy Database has also the highest quality of data since it's completely annotated by EEG experts and contains supplementary Metatdata about evaluations and EEG annotations (clinical manifest and subclinical seizures, interictal events) in a supplementary relational database.

  • gold standard annotations
  • from EEG experts
  • extensive clinical metadata

60 datasets available


2 packages available now:

30 surface patients (€3000)

30 invasive patients (€3000)

  • 3 years license (extensible)
  • restricted to research use.
  • price is valid for non-commercial institutions only.


The more than 200 remaining datasets will be made available later.

The EPILEPSIAE Project Database

Many technological applications in the field of neurology and neuroscience depend on evaluations based on EEG data. So far, public resources for EEG recordings have been limited.
As part of the EPILEPSIAE project an extensive database of long-term recordings of the intracranial and surface EEG was compiled.

This epilepsy database is by far the largest and most comprehensive database for human surface and intracranial eeg data. It is suitable for a broad range of applications e.g. of time series analyses of brain activity. Currently, the EU database contains annotated EEG datasets from more than 250 patients with epilepsy, 50 of them with intracranial recordings with up to 122 channels. Each dataset provides EEG data for a continuous recording time of about 150 hours (> 5 days) on average at a sample rate from 250 Hz up to 2500 Hz. Clinical patient information and MR imaging data (most datasets) supplement the EEG data.



Database in comparison

A comparison between presently available EEG data collections from Bonn [1], Flint Hills [2] and Freiburg [3] and the European EEG/epilepsy database concerning number of datasets, hours of EEG recordings and contained clinical mainfest seizures.

The compared data collections contain considerably less patient datasets (5 in Bonn, 10 in Flint Hills and 21 in Freiburg). The total duration of EEG recordings included exceeds 40000 hours. This is by an order of magnitude more than Bonn (35 hours), Flint Hills (1400 hours) and Freiburg (509 hours) contain in total. Also, the 2400 contained seizures in the EU database exceed the number of contained seizures in Bonn (5), Flint Hills (49) and Freiburg (88).

References

  1. The Bonn EEG database
  2. The Flint Hills Scientific ECoG database
  3. The Freiburg EEG database

Publications


  1. Guendelman M, Vekslar R, Shriki O. A New Perspective in Epileptic Seizure Classification: Applying the Taxonomy of Seizure Dynamotypes to Noninvasive EEG and Examining Dynamical Changes across Sleep Stages. eNeuro. 2025 Jan 16;12(1):ENEURO.0157-24.2024.

  2. Lopes F, Pinto MF, Dourado A, Schulze-Bonhage A, Dümpelmann M, Teixeira C. Addressing data limitations in seizure prediction through transfer learning. Scientific Reports (2024) 1:14169.

  3. Costa G, Teixeira CA, Pinto MF. ‘Comparison between epileptic seizure prediction and forecasting based on machine learning‘. Scientific Reports (2024) 14(1), pp. 5653.

  4. Pontes E, Pinto M, Lopes F, Teixeira CA. ‘Concept-drifts adaptation for machine learning EEG epilepsy seizure prediction‘. Scientific Reports (2024) 14(1), pp. 8204.

  5. Batista J. Pinto MF, Tavares M, Lopes F, Oliveira A, Teixeira CA. ‘EEG epilepsy seizure prediction: the post‑processing stage as a chronology‘. Scientific Reports (2024) 14(1), pp. 407.

  6. Oliveira, A. Pinto MF, Lopes F, Leal A, Teixeira CA. ‘Classifier combination supported by the sleep-wake cycle improves EEG seizure prediction performance’. IEEE Transactions on Biomedical Engineering (2024) XX(XX), pp. 1–11.

  7. Pinto MF, Batista1 J, Leal A, Lopes F, Oliveira A, Dourado A, Abuhaiba SI, Sales F, Martins P, Teixeira CA. ‘The goal of explaining black boxes in EEG seizure prediction is not to explain models’ decisions’, Epilepsia Open (2023), 8(2), pp. 285–297.

  8. Gelbard-Sagiv H,Pardo S, Getter N, Guendelman M, Benninger F, Kraus D, Shriki O, Ben-Sasson S. (2023) ‘Optimizing Electrode Configurations for Wearable EEG Seizure Detection Using Machine Learning’, Sensors, 23(13), p. 5805.

  9. Lopes F, Leal A, Pinto MF, Dourado A, Schulze-Bonhage A, Dümpelmann M, Teixeira C. ‘Removing artefacts and periodically retraining improve performance of neural network-based seizure prediction models.‘ Scientific Reports (2023) 1, 5918.

  10. Lopes F, Leal A, Medeiros J, Pinto MF, Dourado A, Dümpelmann M, Teixeira C. EPIC: Annotated epileptic EEG independent components for artifact reduction. Scientific Data (2022) 9:Article number: 512.

  11. Fouad A, Azizollahi H, Le Douget JE, Lejeune FX, Valderrama M, Mayor L, Navarro V, Le Van Quyen M. Interictal epileptiform discharges show distinct spatiotemporal and morphological patterns across wake and sleep. Brain Commun. 2022 Jul 18;4(5):fcac183.

  12. Yang, Y. et al. (2022) ‘A multimodal AI system for out-of-distribution generalization of seizure identification’, IEEE Journal of Biomedical and Health Informatics. IEEE, 2194(c), pp. 1–10. doi: 10.1109/JBHI.2022.3157877.

  13. Baghersalimi, S. et al. (2022) ‘Personalized Real-Time Federated Learning for Epileptic Seizure Detection’, IEEE Journal of Biomedical and Health Informatics. IEEE, 26(2), pp. 898–909. doi: 10.1109/JBHI.2021.3096127.

  14. Sopic, D. et al. (2022) ‘Personalized seizure signature: An interpretable approach to false alarm reduction for long‐term epileptic seizure detection’, Epilepsia, (January), pp. 1–11. doi: 10.1111/epi.17176.

  15. Sanz-García, A., Perez-Romero, M. and Ortega, G. J. (2022) ‘Spectral and network characterization of focal seizure types and phases’, Computer Methods and Programs in Biomedicine. Elsevier B.V., p. 106704. doi: 10.1016/j.cmpb.2022.106704.

  16. Lopes, F. et al. (2022) ‘Ensemble Deep Neural Network for Automatic Classification of EEG Independent Components’, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 30, pp. 1–1. doi: 10.1109/tnsre.2022.3154891.

  17. Pinto, M. et al. (2022) ‘Interpretable EEG seizure prediction using a multiobjective evolutionary algorithm’, Scientific Reports. Nature Publishing Group UK, (0123456789), pp. 1–15. doi: 10.1038/s41598-022-08322-w.

  18. Leal, A. et al. (2021) ‘Heart rate variability analysis for the identification of the preictal interval in patients with drug-resistant epilepsy’, Scientific Reports. Nature Publishing Group UK, 11(1), pp. 1–11. doi: 10.1038/s41598-021-85350-y.

  19. Lopes, F. et al. (2021) ‘Automatic Electroencephalogram Artifact Removal Using Deep Convolutional Neural Networks’, IEEE Access, 9, pp. 149955–149970. doi: 10.1109/ACCESS.2021.3125728.

  20. Vandecasteele, K. et al. (2021) ‘The power of ECG in multimodal patient-specific seizure monitoring: Added value to an EEG‐based detector using limited channels’, Epilepsia, (January), p. epi.16990. doi: 10.1111/epi.16990.

  21. Pinto, M. F. et al. (2021) ‘A personalized and evolutionary algorithm for interpretable EEG epilepsy seizure prediction’, Scientific Reports. Nature Publishing Group UK, 11(1), p. 3415. doi: 10.1038/s41598-021-82828-7.

  22. Liu, T. et al. (2020) ‘Epileptic Seizure Classification With Symmetric and Hybrid Bilinear Models’, IEEE Journal of Biomedical and Health Informatics. IEEE, 24(10), pp. 2844–2851. doi: 10.1109/JBHI.2020.2984128.

  23. Gómez, C. et al. (2020) ‘Automatic seizure detection based on imaged-EEG signals through fully convolutional networks’, Scientific Reports. Nature Publishing Group UK, 10(1), p. 21833. doi: 10.1038/s41598-020-78784-3.

  24. Stojanović, O., Kuhlmann, L. and Pipa, G. (2020) ‘Predicting epileptic seizures using nonnegative matrix factorization’, PLOS ONE. Edited by L. M. Ward, 15(2), p. e0228025. doi: 10.1371/journal.pone.0228025.

  25. Saggio, M. L. et al. (2020) ‘A taxonomy of seizure dynamotypes’, eLife, 9, pp. 1–56. doi: 10.7554/eLife.55632.

  26. Heller, S. et al. (2018) ‘Hardware Implementation of a Performance and Energy-optimized Convolutional Neural Network for Seizure Detection’, in 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2268–2271. doi: 10.1109/EMBC.2018.8512735.

  27. Manzouri, F. et al. (2018) ‘A Comparison of Machine Learning Classifiers for Energy-Efficient Implementation of Seizure Detection’, Frontiers in Systems Neuroscience, 12(September), pp. 1–11. doi: 10.3389/fnsys.2018.00043.

  28. Hügle, M. et al. (2018) ‘Early Seizure Detection with an Energy-Efficient Convolutional Neural Network on an Implantable Microcontroller’, in 2018 International Joint Conference on Neural Networks (IJCNN). Rio de Janeiro: IEEE, pp. 1–7. doi: 10.1109/IJCNN.2018.848949.

  29. Donos, C. et al. (2018) ‘Seizure onset predicts its type’, Epilepsia, 59(December 2017), pp. 650–660. doi: 10.1111/epi.13997.

  30. Manzouri, F. et al. (2017) ‘Optimized detector for closed-loop devices for neurostimulation’, in 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, pp. 2158–2163. doi: 10.1109/SMC.2017.8122939.

  31. Ferastraoaru, V. et al. (2016) ‘Termination of seizure clusters is related to the duration of focal seizures’, Epilepsia, 57(6), pp. 889–895. doi: 10.1111/epi.13375.

  32. Meisel, C. et al. (2016) ‘Quantifying antiepileptic drug effects using intrinsic excitability measures’, Epilepsia, 57(11), pp. e210–e215. doi: 10.1111/epi.13517.

  33. Qaraqe, M. et al. (2016) ‘Epileptic seizure onset detection based on EEG and ECG data fusion’, Epilepsy & Behavior, 58, pp. 48–60. doi: 10.1016/j.yebeh.2016.02.039.

  34. Direito, B. et al. (2016) ‘A Realistic Seizure Prediction Study Based on Multiclass SVM’, International Journal of Neural Systems, 27(3), p. 1750006. doi: 10.1142/S012906571750006X.

  35. Donos, C., Dümpelmann, M. and Schulze-Bonhage, A. (2015) ‘Early Seizure Detection Algorithm Based on Intracranial EEG and Random Forest Classification.’, International journal of neural systems, 25(5), p. 1550023. doi: 10.1142/S0129065715500239.

  36. Alvarado-Rojas, C. et al. (2015) ‘Slow modulations of high-frequency activity (40–140 Hz) discriminate preictal changes in human focal epilepsy’, Scientific Reports, 4(1), p. 4545. doi: 10.1038/srep04545.

  37. Meisel, C. et al. (2015) ‘Intrinsic excitability measures track antiepileptic drug action and uncover increasing/decreasing excitability over the wake/sleep cycle’, Proceedings of the National Academy of Sciences, 112(47), pp. 14694–14699. doi: 10.1073/pnas.1513716112.

  38. Bandarabadi, M. et al. (2014) ‘Sub-band Mean Phase Coherence for Automated Epileptic Seizure Detection’, in IFMBE Proceedings, pp. 319–322. doi: 10.1007/978-3-319-03005-0.

  39. Bandarabadi, M. et al. (2014) ‘Robust and low complexity algorithms for seizure detection’, in 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, pp. 4447–4450. doi: 10.1109/EMBC.2014.6944611.

  40. Alexandre Teixeira, C. et al. (2014) ‘Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients’, Computer Methods and Programs in Biomedicine. Elsevier Ireland Ltd, 114(3), pp. 324–336. doi: 10.1016/j.cmpb.2014.02.007.

  41. Klatt, J. et al. (2012) ‘The EPILEPSIAE database: An extensive electroencephalography database of epilepsy patients’, Epilepsia, 53(9), pp. 1669–1676. doi: 10.1111/j.1528-1167.2012.03564.x.

  42. Ihle, M. et al. (2012) ‘EPILEPSIAE – A European epilepsy database’, Computer Methods and Programs in Biomedicine. Elsevier Ireland Ltd, 106(3), pp. 127–138. doi: 10.1016/j.cmpb.2010.08.011.

  43. Teixeira, C. A. et al. (2011) ‘EPILAB: A software package for studies on the prediction of epileptic seizures’, Journal of Neuroscience Methods. Elsevier B.V., 200(2), pp. 257–271. doi: 10.1016/j.jneumeth.2011.07.002.

  44. Feldwisch-Drentrup, H. et al. (2011) ‘Anticipating the unobserved: Prediction of subclinical seizures’, Epilepsy and Behavior. Elsevier Inc., 22(SUPPL. 1), pp. S119–S126. doi: 10.1016/j.yebeh.2011.08.023.

    45. This list may miss some publications using data from the EPILEPSIAE database. Please send information on missing publications to:

    matthias.duempelmann(at)uniklinik-freiburg.de

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